LearningModelSessionOptions.BatchSizeOverride Property

Definition

Namespace:

Windows.AI.MachineLearning

A numeric value that specifies a constant batch size override value for the target model.

public:
 property unsigned int BatchSizeOverride { unsigned int get(); void set(unsigned int value); };

uint32_t BatchSizeOverride();

void BatchSizeOverride(uint32_t value);

public uint BatchSizeOverride { get; set; }

var uInt32 = learningModelSessionOptions.batchSizeOverride;
learningModelSessionOptions.batchSizeOverride = uInt32;

Public Property BatchSizeOverride As UInteger

Property Value

UInt32

unsigned int

uint32_t

The constant batch size for model inputs feature values expected by calls to Bind. The default value for the BatchSizeOverride will be 1 indicating a static batch size of 1.

Remarks

The BatchSizeOverride option allows the model compiler to use constant batch size performance optimizations when setting up the LearningModelSession.

By setting the BatchSizeOverride options, all inputs for the session will have their batch sizes restricted to match the specified BatchSizeOverride value.

A BatchSizeOverride = 0 indicates that the batch size present in the model should be honored without change.

BatchSizeOverride > 0 indicates the batch size that will be used to override the model batch size and optimize evaluations.

If model evaluations always happen using the same batch size, performance can be improved by setting the BatchSizeOverride to the expected batch size.

Setting the BatchSizeOverride parameter will restrict the model's input batch size to a constant batch size, and will enable model compiler optimizations that can improve the runtime performance of calls to Evaluate.

If the inference model is designed to accept any batch size, then the BatchSizeOverride parameter can be used to restrict batched inputs to a constant batch size.

However, if the inference model is designed with a constant batch size, the BatchSizeOverride parameter must match the constant batch size defined in the model.

ONNX models describe the batch dimension of input tensor features using the DATA_BATCH dimension denotation. This corresponds to the N dimension in the more commonly used tensor format notation NCHW.